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Artificial Intelligence · Mini Project
Face Detection with OpenCV
Haar cascades versus a DNN face detector, measured head to head
1-2 weeks build
Overview
A compact study of two face detectors in OpenCV: the classic Viola-Jones Haar cascade and the SSD-based DNN model that ships with OpenCV. Both run on images, video and webcam, and the kit measures detection rate, false positives and speed under side profiles, low light and small faces, then presents the trade-offs in plots.
What makes it stand up
- 01Haar cascade detector with tunable scale and neighbours
- 02OpenCV DNN face detector on the same inputs
- 03Webcam, video and image modes from one script
- 04Tests on profile, low-light and small-face cases
- 05Speed and detection comparison plotted in the report
Module breakdown
- 01
Haar detector
Cascade loading and parameter tuning.
- 02
DNN detector
Caffe SSD model with confidence filtering.
- 03
Input modes
Image, video file and webcam handling.
- 04
Test set
Curated hard cases for fair comparison.
- 05
Comparison
Timing and detection counts with charts.
After this, you will be able to
- Explain Haar features and the integral image
- Compare classical and deep detectors with evidence
- Demo both detectors on your webcam
Technology stack
- Python
- OpenCV
- NumPy
- Matplotlib
- Level
- Mini Project
- Domain
- Artificial Intelligence
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support